Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies
Authors: Mathias Gorski, Humaira Rasheed, Alexander Teumer, Laurent F. Thomas, Sarah E. Graham, Gardar Sveinbjornsson, Thomas W. Winkler, Felix Günther, Klaus J. Stark, Jin-Fang Chai, Bamidele O. Tayo, Matthias Wuttke, Yong Li, Adrienne Tin, Tarunveer S. Ahluwalia, Johan Ärnlöv, Bjørn Olav Åsvold, Stephan J.L. Bakker, Bernhard Banas, Nisha Bansal, Mary L. Biggs, Ginevra Biino, Michael Böhnke, Eric Boerwinkle, Erwin P. Bottinger, Hermann Brenner, Ben Brumpton, Robert J. Carroll, Layal Chaker, John Chalmers, Miao-Li Chee, Miao-Ling Chee, Ching-Yu Cheng, Audrey Y. Chu, Marina Ciullo, Massimiliano Cocca, James P. Cook, Josef Coresh, Daniele Cusi, Martin H. de Borst, Frauke Degenhardt, Kai-Uwe Eckardt, Karlhans Endlich, Michele K. Evans, Mary F. Feitosa, Andre Franke, Sandra Freitag-Wolf, Christian Fuchsberger, Piyush Gampawar, Ron T. Gansevoort, Mohsen Ghanbari, Sahar Ghasemi, Vilmantas Giedraitis, Christian Gieger, Daniel F. Gudbjartsson, Stein Hallan, Pavel Hamet, Asahi Hishida, Kevin Ho, Edith Hofer, Bernd Holleczek, Hilma Holm, Anselm Hoppmann, Katrin Horn, Nina Hutri-Kähönen, Kristian Hveem, Shih-Jen Hwang, M. Arfan Ikram, Navya Shilpa Josyula, Bettina Jung, Mika Kähönen, Irma Karabegović, Chiea-Chuen Khor, Wolfgang Koenig, Holly Kramer, Bernhard K. Krämer, Brigitte Kühnel, Johanna Kuusisto, Markku Laakso, Leslie A. Lange, Terho Lehtimäki, Man Li, Wolfgang Lieb, Lars Lind, Cecilia M. Lindgren, Ruth J.F. Loos, Mary Ann Lukas, Leo-Pekka Lyytikäinen, Anubha Mahajan, Pamela R. Matias-Garcia, Christa Meisinger, Thomas Meitinger, Olle Melander, Yuri Milaneschi, Pashupati P. Mishra, Nina Mononen, Andrew P. Morris, Josyf C. Mychaleckyj, Girish N. Nadkarni, Mariko Naito, Masahiro Nakatochi, Mike A. Nalls, Matthias Nauck, Kjell Nikus, Boting Ning, Ilja M. Nolte, Teresa Nutile, Michelle L. O’Donoghue, Jeffrey O'Connell, Isleifur Olafsson, Marju Orho-Melander, Afshin Parsa, Sarah A. Pendergrass, Brenda W.J.H. Penninx, Mario Pirastu, Michael H. Preuss, Bruce M. Psaty, Laura M. Raffield, Olli T. Raitakari, Myriam Rheinberger, Kenneth M. Rice, Federica Rizzi, Alexander R. Rosenkranz, Peter Rossing, Jerome I. Rotter, Daniela Ruggiero, Kathleen A. Ryan, Charumathi Sabanayagam, Erika Salvi, Helena Schmidt, Reinhold Schmidt, Markus Scholz, Ben Schöttker, Christina-Alexandra Schulz, Sanaz Sedaghat, Christian M. Shaffer, Karsten B. Sieber, Xueling Sim, Mario Sims, Harold Snieder, Kira J. Stanzick, Unnur Thorsteinsdottir, Hannah Stocker, Konstantin Strauch, Heather M. Stringham, Patrick Sulem, Silke Szymczak, Kent D. Taylor, Chris H.L. Thio, Johanne Tremblay, Simona Vaccargiu, Pim van der Harst, Peter J. van der Most, Niek Verweij, Uwe Völker, Kenji Wakai, Melanie Waldenberger, Lars Wallentin, Stefan Wallner, Judy Wang, Dawn M. Waterworth, Harvey D. White, Cristen J. Willer, Tien-Yin Wong, Mark Woodward, Qiong Yang, Laura M. Yerges-Armstrong, Martina Zimmermann, Alan B. Zonderman, Tobias Bergler, Kari Stefansson, Carsten A. Böger, Cristian Pattaro, Anna Köttgen, Florian Kronenberg, Iris M. Heid
Journal: Kidney International (2022)

Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure (95% confidence intervals 1.03-1.77) and 1.27 for acute kidney injury (95% confidence intervals 1.08-1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics. Estimated glomerular filtration rate (eGFR) reflects kidney function. Progressive eGFR-decline can lead to kidney failure, necessitating dialysis or transplantation. Hundreds of loci from genome-wide association studies (GWAS) for eGFR help explain population cross section variability. Since the contribution of these or other loci to eGFR-decline remains largely unknown, we derived GWAS for annual eGFR-decline and meta-analyzed 62 longitudinal studies with eGFR assessed twice over time in all 343,339 individuals and in high-risk groups. We also explored different covariate adjustment. Twelve genome-wide significant independent variants for eGFR-decline unadjusted or adjusted for eGFR-baseline (11 novel, one known for this phenotype), including nine variants robustly associated across models were identified. All loci for eGFR-decline were known for cross-sectional eGFR and thus distinguished a subgroup of eGFR loci. Seven of the nine variants showed variant-by-age interaction on eGFR cross section (further about 350,000 individuals), which linked genetic associations for eGFR-decline with age-dependency of genetic cross-section associations. Clinically important were two to four-fold greater genetic effects on eGFR-decline in high-risk subgroups. Five variants associated also with chronic kidney disease progression mapped to genes with functional in-silico evidence (UMOD, SPATA7, GALNTL5, TPPP). An unfavorable versus favorable nine-variant genetic profile showed increased risk odds ratios of 1.35 for kidney failure (95% confidence intervals 1.03-1.77) and 1.27 for acute kidney injury (95% confidence intervals 1.08-1.50) in over 2000 cases each, with matched controls). Thus, we provide a large data resource, genetic loci, and prioritized genes for kidney function decline, which help inform drug development pipelines revealing important insights into the age-dependency of kidney function genetics. Glomerular filtration rate (GFR) is accepted as best overall index of kidney function.1Levey A.S. Coresh J. Tighiouart H. et al.Measured and estimated glomerular filtration rate: current status and future directions.Nat Rev Nephrol. 2020; 16: 51-64Crossref PubMed Scopus (128) Google Scholar A GFR <60 ml/min per 1.73 m2 defines chronic kidney disease (CKD),2Andrassy K.M. Comments on "KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease.".Kidney Int. 2013; 84: 622-623Abstract Full Text Full Text PDF PubMed Scopus (425) Google Scholar which affects about 10% of adults.3Eckardt K.-U. Coresh J. Devuyst O. et al.Evolving importance of kidney disease: from subspecialty to global health burden.Lancet. 2013; 382: 158-169Abstract Full Text Full Text PDF PubMed Scopus (827) Google Scholar A decline in GFR over time is characteristic for CKD progression, which can lead to kidney failure,4Neuen B.L. Weldegiorgis M. Herrington W.G. et al.Changes in GFR and albuminuria in routine clinical practice and the risk of kidney disease progression.Am J Kidney Dis. 2021; 78: 350-360.e1Abstract Full Text Full Text PDF PubMed Scopus (17) Google Scholar requiring dialysis or kidney transplantation, with a high risk of premature mortality.5Meguid El Nahas A. Bello A.K. Chronic kidney disease: the global challenge.Lancet. 2005; 365: 331-340Abstract Full Text Full Text PDF PubMed Scopus (925) Google Scholar In population studies on kidney function, estimated GFR (eGFR) is usually derived from serum creatinine,6Levey A.S. Stevens L.A. Schmid C.H. et al.A new equation to estimate glomerular filtration rate.Ann Intern Med. 2009; 150: 604-612Crossref PubMed Scopus (17790) Google Scholar and annual eGFR decline is the difference between 2 such assessments divided by the years between these assessments. Decline in eGFR is age related, with a physiological loss of ≈1 ml/min per 1.73 m2 per year2Andrassy K.M. Comments on "KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease.".Kidney Int. 2013; 84: 622-623Abstract Full Text Full Text PDF PubMed Scopus (425) Google Scholar generally and 3 ml/min per 1.73 m2 per year in the presence of diabetes mellitus (DM), a major risk factor for CKD progression.7Levin A. Stevens P.E. Bilous R.W. et al.Notice. Kidney Int Suppl. 2013; 3: 1Abstract Full Text Full Text PDF Scopus (1824) Google Scholar,8Hemmelgarn B.R. Zhang J. Manns B.J. et al.Progression of kidney dysfunction in the community-dwelling elderly.Kidney Int. 2006; 69: 2155-2161Abstract Full Text Full Text PDF PubMed Scopus (312) Google Scholar Therapeutic options to decelerate kidney function decline are limited. In addition to pharmacologic inhibitors of the renin-angiotensin-aldosterone system,9Garcia Sanchez J.J. Thompson J. Scott D.A. et al.Treatments for chronic kidney disease: a systematic literature review of randomized controlled trials.Adv Ther. 2022; 39: 193-220Crossref PubMed Scopus (9) Google Scholar the recent introduction of sodium-glucose cotransporter-2 (SGLT2) inhibitors shows promising renoprotective effects.10Heerspink H.J.L. Stefánsson B.V. Correa-Rotter R. et al.Dapagliflozin in patients with chronic kidney disease.N Engl J Med. 2020; 383: 1436-1446Crossref PubMed Scopus (2042) Google Scholar,11Borges-Júnior F.A. Silva dos Santos D. Benetti A. et al.Empagliflozin inhibits proximal tubule NHE3 activity, preserves GFR, and restores euvolemia in nondiabetic rats with induced heart failure.J Am Soc Nephrol. 2021; 32: 1616-1629Crossref PubMed Scopus (34) Google Scholar An understanding of the mechanisms of kidney function decline and the development of new therapeutic options is thus of high clinical and public health relevance.7Levin A. Stevens P.E. Bilous R.W. et al.Notice. Kidney Int Suppl. 2013; 3: 1Abstract Full Text Full Text PDF Scopus (1824) Google Scholar,12Abbafati C. Abbas K.M. Abbasi-Kangevari M. et al.Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019.Lancet. 2020; 396: 1204-1222Abstract Full Text Full Text PDF PubMed Scopus (5989) Google Scholar Genes underneath genome-wide association study (GWAS) loci for diseases and biomarkers help identify new therapies.13King E.A. Davis J.W. Degner J.F. Are drug targets with genetic support twice as likely to be approved? revised estimates of the impact of genetic support for drug mechanisms on the probability of drug approval.PLoS Genet. 2019; 15e1008489Crossref PubMed Scopus (244) Google Scholar Open access GWAS summary statistics from large sample sizes are a highly queried resource, also for causal inference studies.14Buniello A. Macarthur J.A.L. Cerezo M. et al.The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019.Nucleic Acids Res. 2019; 47: D1005-D1012Crossref PubMed Scopus (2110) Google Scholar Hundreds of loci and genes are identified by cross-sectional GWAS for eGFR (i.e., GWAS for eGFR based on a single serum creatinine measurement),15Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (362) Google Scholar, 16Hellwege J.N. Velez Edwards D.R. Giri A. et al.Mapping eGFR loci to the renal transcriptome and phenome in the VA Million Veteran Program.Nat Commun. 2019; 10: 3842Crossref PubMed Scopus (71) Google Scholar, 17Chambers J.C. Zhang W. Lord G.M. et al.Genetic loci influencing kidney function and chronic kidney disease.Nat Genet. 2010; 42: 373-375Crossref PubMed Scopus (225) Google Scholar, 18Stanzick K.J. Li Y. Schlosser P. et al.Discovery and prioritization of variants and genes for kidney function in >1.2 million individuals.Nat Commun. 2021; 12: 4350Crossref PubMed Scopus (75) Google Scholar which help explain population variability. However, the mechanisms underlying a genetic variant association with lower but stable eGFR over time might not always be disease relevant. GWASs on parameters more directly linked to disease progression are thought to better inform drug development.19Paternoster L. Tilling K. Davey Smith G. Genetic epidemiology and Mendelian randomization for informing disease therapeutics: conceptual and methodological challenges.PLoS Genet. 2017; 13e1006944Crossref Scopus (150) Google Scholar Current evidence from GWASs on annual eGFR decline is limited, owed to substantial logistics in conducting longitudinal studies and thus small sample sizes. Only one variant, in the UMOD-PDILT locus, has been identified at genome-wide significance20Gorski M. Tin A. Garnaas M. et al.Genome-wide association study of kidney function decline in individuals of European descent.Kidney Int. 2015; 87: 1017-1029Abstract Full Text Full Text PDF PubMed Scopus (103) Google Scholar (n ≈ 60,000). With an estimated heritability of 38% for annual eGFR decline,20Gorski M. Tin A. Garnaas M. et al.Genome-wide association study of kidney function decline in individuals of European descent.Kidney Int. 2015; 87: 1017-1029Abstract Full Text Full Text PDF PubMed Scopus (103) Google Scholar comparable to 33% to 39% estimated for cross-sectional eGFR in general populations,15Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (362) Google Scholar,21Fox C.S. Yang Q. Cupples L.A. et al.Genomewide linkage analysis to serum creatinine, GFR, and creatinine clearance in a community-based population: the Framingham Heart Study.J Am Soc Nephrol. 2004; 15: 2457-2461Crossref PubMed Scopus (142) Google Scholar much more can be expected in larger sample sizes. Further 3 loci were genome-wide significant in an extreme phenotype approach, comparing individuals with large eGFR decline or steep decrease into CKD with respective controls.22Gorski M. Jung B. Li Y. et al.Meta-analysis uncovers genome-wide significant variants for rapid kidney function decline.Kidney Int. 2021; 99: 926-939Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar Although these are important binary clinical end points, methodological literature supports the use of regression methods on undichotomized variables.23MacCallum R.C. Zhang S. Preacher K.J. Rucker D.D. On the practice of dichotomization of quantitative variables.Psychol Methods. 2002; 7: 19-40Crossref PubMed Scopus (2386) Google Scholar The limited availability of longitudinal GWASs is not only an issue for kidney function decline, but also generally (e.g., change in lung function [n = 27,24924Tang W. Kowgier M. Loth D.W. et al.Large-scale genome-wide association studies and meta-analyses of longitudinal change in adult lung function.PLoS One. 2014; 9e100776Google Scholar], glucose [n = 13,80725Liu C.-T. Merino J. Rybin D. et al.Genome-wide association study of change in fasting glucose over time in 13,807 non-diabetic European ancestry individuals.Sci Rep. 2019; 9: 9439Crossref PubMed Scopus (5) Google Scholar], or blood pressure [n = 33,72026Gouveia M.H. Bentley A.R. Leonard H. et al.Trans-ethnic meta-analysis identifies new loci associated with longitudinal blood pressure traits.Sci Rep. 2021; 11: 4075Crossref PubMed Scopus (8) Google Scholar]); consequently, locus findings on biomarker change are few and often unstable.14Buniello A. Macarthur J.A.L. Cerezo M. et al.The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019.Nucleic Acids Res. 2019; 47: D1005-D1012Crossref PubMed Scopus (2110) Google Scholar A challenge beyond power is limited experience in longitudinal GWASs with regard to covariate adjustment: clinical trials for disease-related biomarker change require control for differences in baseline levels between therapy groups.27Vickers A.J. Altman D.G. Statistics notes: analysing controlled trials with baseline and follow up measurements.BMJ. 2001; 323: 1123-1124Crossref PubMed Google Scholar However, covariate adjustment in GWASs requires a careful choice28Aschard H. Vilhjálmsson B.J. Joshi A.D. et al.Adjusting for heritable covariates can bias effect estimates in genome-wide association studies.Am J Hum Genet. 2015; 96: 329-339Abstract Full Text Full Text PDF PubMed Scopus (167) Google Scholar: it can reveal important mediator effects (e.g., DM adjusted for body mass index29Frayling T.M. Timpson N.J. Weedon M.N. et al.A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity.Science. 2007; 316: 889-894Crossref PubMed Scopus (3504) Google Scholar), alter the phenotype (e.g., waist-to-hip ratio "unexpected" by body mass index28Aschard H. Vilhjálmsson B.J. Joshi A.D. et al.Adjusting for heritable covariates can bias effect estimates in genome-wide association studies.Am J Hum Genet. 2015; 96: 329-339Abstract Full Text Full Text PDF PubMed Scopus (167) Google Scholar,30Winkler T.W. Günther F. Höllerer S. et al.A joint view on genetic variants for adiposity differentiates subtypes with distinct metabolic implications.Nat Commun. 2018; 9: 1946Crossref PubMed Scopus (28) Google Scholar), and yield artefacts from heritable covariates (collider bias28Aschard H. Vilhjálmsson B.J. Joshi A.D. et al.Adjusting for heritable covariates can bias effect estimates in genome-wide association studies.Am J Hum Genet. 2015; 96: 329-339Abstract Full Text Full Text PDF PubMed Scopus (167) Google Scholar) or nonsense association (e.g., sex adjusted for height31Day F.R. Loh P.-R. Scott R.A. et al.A robust example of collider bias in a genetic association study.Am J Hum Genet. 2016; 98: 392-393Abstract Full Text Full Text PDF PubMed Scopus (63) Google Scholar). The impact of covariate adjustment on longitudinal GWASs on eGFR decline, and biomarker change generally, is not well explored. We thus aimed to identify genetic loci associated with annual eGFR decline and CKD progression (defined as eGFR decline among individuals with CKD at baseline) and to prioritize genes that may inform drug development for slowing down eGFR decline and CKD progression. We also aimed to fill the gap of large-data genome-wide single-nucleotide polymorphism (SNP) summary statistics for annual eGFR decline and CKD progression, to help future meta-analyses and Mendelian randomization studies. Finally, we wanted to understand the impact of different covariate adjustment and whether an SNP associated with eGFR decline showed an age-dependent association on eGFR cross-sectionally (i.e., SNP-by-age interaction on eGFR cross-sectionally). By this, we aimed to contribute to a better understanding of the interpretation of genetic findings for eGFR decline and other progression traits. To achieve these aims, we (i) increased sample size for GWASs on annual eGFR decline to >340,000 individuals based on the CKD Genetics (CKDGen) consortium32Köttgen A. Pattaro C. The CKDGen Consortium: ten years of insights into the genetic basis of kidney function.Kidney Int. 2020; 97: 236-242Abstract Full Text Full Text PDF PubMed Scopus (19) Google Scholar and UK Biobank,33Sudlow C. Gallacher J. Allen N. et al.UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age.PLoS Med. 2015; 12e1001779Crossref PubMed Scopus (4509) Google Scholar (ii) applied a suite of covariate adjustment models, (iii) analyzed SNP-by-age interaction on eGFR cross-sectionally in >350,000 individuals independent of the GWAS on decline, and (v) conducted genetic risk score (GRS) analyses for acute kidney injury (AKI) and end-stage kidney disease (ESKD). We conducted GWAS meta-analysis based on study-specific summary statistics. Each study utilized data on 2 measurements of serum creatinine over time and genome-wide SNP information imputed to 1000 Genomes34The 1000 Genomes Project ConsortiumAn integrated map of genetic variation supplementary material.Nature. 2012; 135: 1-113Google Scholar phase 1 or phase 3, the Haplotype Reference Consortium35McCarthy S. Das S. Kretzschmar W. et al.A reference panel of 64,976 haplotypes for genotype imputation.Nat Genet. 2016; 48: 1279-1283Crossref PubMed Scopus (1601) Google Scholar v1.1, or similar group (Supplementary Tables S1 and S2). Serum creatinine, measured at baseline and follow-up, was used to estimate eGFR at baseline and follow-up, respectively, according to the CKD Epidemiology Collaboration equation.6Levey A.S. Stevens L.A. Schmid C.H. et al.A new equation to estimate glomerular filtration rate.Ann Intern Med. 2009; 150: 604-612Crossref PubMed Scopus (17790) Google Scholar Annual eGFR decline was defined as "–(eGFR at follow-up – eGFR at baseline) / number of years of follow-up." GWAS analyses were conducted separately by ancestry (if applicable), where ancestry was defined by genetic principal components or participants' self-report. GWASs were based on linear regression with different covariate adjustment conducted overall and focused on individuals with DM or CKD at baseline. Study-specific genome-wide summary statistics and detailed phenotype information were transferred to the meta-analysis center. For each SNP, summary statistics were pooled and genomic control was corrected. Significant genetic variants were identified, and respective locus regions were selected. In addition, we investigated identified SNPs for SNP-by-age interaction on cross-sectional eGFR (based on creatinine or cystatin C [eGFRcrea or eGFRcys, respectively]) using UK Biobank data that were independent of the SNP identification step (excluding the individuals in the decline GWAS). We computed the GRS and its association on eGFR decline in The Trøndelag Health Study (The HUNT study) via linear regression and provided odds ratios (ORs) for GRS association in case-control studies on AKI and ESKD via logistic regression. Detailed methods are provided in the Supplementary Methods. This GWAS meta-analysis included 343,339 individuals from 62 studies (Supplementary Tables S1 and S3, Supplementary Figure S1, and Methods) and 12,403,901 analyzable SNPs. Most studies were population based (76%) and of European ancestry (74%). Study-specific median annual eGFR decline was independent of sample size and follow-up length (Supplementary Figure S2A and B), and the median across studies was 1.32 ml/min per 1.73 m2 per year; follow-up length was 1 to 21 years (median [25th–75th percentile], 5 [4–7] years); and median age ranged from 33 to 77 years (Supplementary Figure S2C). All analyses were adjusted for age-, sex-, and study-specific covariates, which is not mentioned further from here on (stable across different modes of age adjustment; Supplementary Figure S3). We had 5 GWAS results for eGFR decline (Methods): (i) "unadjusted," (ii) "DM adjusted," (iii) "adjusted for eGFR baseline," (iv) restricted to individuals with DM at baseline (unadjusted), and (v) restricted to individuals with CKD at baseline (unadjusted). There is, to date, no standard conduct for GWASs on eGFR decline with regard to covariate adjustment. We explored the impact of 2 potentially important covariates additional to age and sex: (i) DM, as an important risk factors for eGFR decline and potential mediator; and (ii) eGFR at baseline, as adjustment for baseline levels in analyses of change over time, has noted pros (larger effects and better detectability) and cons (biased effects).36Glymour M.M. Weuve J. Berkman L.F. et al.When is baseline adjustment useful in analyses of change? an example with education and cognitive change.Am J Epidemiol. 2005; 162: 267-278Crossref PubMed Scopus (502) Google Scholar,37Yanez N.D. Kronmal R.A. Shemanski L.R. The effects of measurement error in response variables and tests of association of explanatory variables in change models.Stat Med. 1998; 17: 2597-2606Crossref PubMed Scopus (58) Google Scholar With regard to DM adjustment, this model was computed in all studies (n = 343,339; 62 studies) and compared with unadjusted results for a subset of studies of varying scope (n = 103,970). DM-adjusted SNP associations on eGFR decline were precisely the same as unadjusted, in terms of β estimates and SEs (Supplementary Figure S4A and Supplementary Note S1). We therefore did not distinguish these 2 models further. In contrast, adjustment for eGFR baseline altered SNP associations on eGFR decline (Supplementary Figure S4B). Therefore, results from both eGFR decline unadjusted and adjusted for eGFR baseline were evaluated in the following. GWAS summary statistics for eGFR decline adjusted for eGFR baseline were formula derived from GWAS summary statistics for unadjusted eGFR decline and for eGFR baseline together with study-specific phenotypic information (Supplementary Note S2). In a subset of studies (n = 103,970), we validated that the formula approach worked well in our setting (Supplementary Note S3 and Supplementary Figure S4C and D). Meta-analysis yielded GWAS results for eGFR decline adjusted for eGFR baseline for 320,737 individuals (50 studies; Supplementary Figure S1). First, our genome-wide screen for eGFR decline unadjusted for eGFR baseline (n = 343,339) identified 2 genome-wide significant independent variants near UMOD-PDILT (PDECLINE < 5 × 10–8; Figure 1a and Table 1): rs34882080, highly correlated with rs12917707 identified previously for this phenotype (r2 = 1.00)20Gorski M. Tin A. Garnaas M. et al.Genome-wide association study of kidney function decline in individuals of European descent.Kidney Int. 2015; 87: 1017-1029Abstract Full Text Full Text PDF PubMed Scopus (103) Google Scholar; and rs77924615, known for altering UMOD expression and urine uromodulin15Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (362) Google Scholar and genome-wide significant for eGFR decline for the first time.Table 1Twelve independent variants in 11 loci identified for association with eGFR decline unadjusted and adjusted for eGFR baselineSNPIDLocus nameChrPosEA/OAEAFDeclineDeclineadjCross-sectionalβPβPβPFrom GWAS/candidate search for decline (baseline-unadjusted)aTwo variants were identified with genome-wide significance for eGFR decline baseline-unadjusted (UMOD-PDILT; Pdecline < 5 × 10–8) and 2 further variants in a candidate search of the 263 variants known for cross-sectional eGFR15 outside UMOD-PDILT, judged at Bonferroni-corrected significance (Pdecline < 0.05/263 = 1.90 × 10–4; PRKAG2 and SPATA7).rs34882080UMOD-PDILT1620, 361, 441a/g0.8150.0652.45 × 10–300.0923.31 × 10–62–0.0092.86 × 10–95rs77924615UMOD-PDILT1620, 392, 332g/a0.7980.0745.30 × 10–380.0993.75 × 10–69–0.0101.45 × 10–138rs10254101PRKAG2dIn PRKAG2 and SPATA7 loci, variants with smallest Pdecline (rs73158188 and rs7160717, respectively) were highly correlated with these candidate-based variants (r2 = 1.00 and 0.93, respectively).7151, 415, 536t/c0.2760.0204.10 × 10–50.0371.78 × 10–14–0.0071.85 × 10–67rs1028455SPATA7dIn PRKAG2 and SPATA7 loci, variants with smallest Pdecline (rs73158188 and rs7160717, respectively) were highly correlated with these candidate-based variants (r2 = 1.00 and 0.93, respectively).1488, 829, 975t/a0.6570.0215.90 × 10–60.0243.43 × 10–8–0.0024.78 × 10–10From GWAS for declineadj, with association for decline (baseline-unadjusted)bFive variants were identified with genome-wide significance for eGFR decline baseline-adjusted AND Bonferroni-corrected significant baseline-unadjusted (Pdecline-adj-BL < 5 × 10–8, and Pdecline < 0.05/12 = 4.17 × 10–3).rs1458038FGF5481, 164, 723c/t0.6900.0193.87 × 10–50.0286.85 × 10–10–0.0037.49 × 10–24rs4930319OVOL11165, 555, 458c/g0.3330.0159.93 × 10–40.0285.27 × 10–10–0.0032.21 × 10–24rs434215TPPPeBecause the TPPP locus lead variant had imputation quality <0.6 in 45% of the studies (median, 0.64), we analyzed this locus omitting the imputation quality filter (with filter: declineadj β = 0.033, P = 1.00 × 10–8; decline β = 0.015, P = 0.039; median imputation quality = 0.74).5699, 046a/g0.2770.0203.70 × 10–40.0327.19 × 10–9–0.0037.63 × 10–6rs28857283C15ORF54fIn the C15ORF54 locus, the identified lead variant for decline was highly correlated with a second signal lead variant for cross-sectional eGFR (rs28833881; r2 = 0.90), but not with the first signal lead variant (rs12913015; r2 = 0.04).1539, 224, 711g/a0.6560.0211.47 × 10–60.0301.31 × 10–11–0.0026.20 × 10–9rs13095391ACVR2B338, 447, 232a/c0.5020.0171.77 × 10–40.0254.03 × 10–8–0.0036.57 × 10–15From GWAS for declineadj, without association for decline (baseline-unadjusted)cThree variants were identified with genome-wide significance for eGFR decline baseline-adjusted but not significantly associated baseline-unadjusted (Pdecline-adj-BL < 5 × 10–8, and Pdecline ≥ 4.17 × 10–3). For each identified variant, we show results for decline (baseline-unadjusted), for decline baseline-adjusted, and for cross-sectional eGFR.15 The β estimates are in ml/min per 1.73 m2 per year and per faster-decline allele; significant P values are stated in bold.rs9998485SHROOM3477, 362, 445a/g0.4660.0070.1560.0279.84 × 10–9–0.0051.22 × 10–41rs1047891CPS12211, 540, 507a/c0.2930.0040.4410.0291.15 × 10–9–0.0071.18 × 10–75rs2453533GATM1545, 641, 225a/c0.4220.0020.7100.0291.72 × 10–11–0.0094.57 × 10–141β, genetic effect coefficient of association; Chr, chromosome on GRCh37; Declineadj, adjusted decline; EAF, effect allele frequency; EA/OA, effect allele/other allele; eGFR, estimated glomerular filtration rate; GWAS, genome-wide association study; Locus name, nearest gene; Pos, position on GRCh37; P, association P value; SNPID, variant identifier on GRCh37.We conducted GWASs for eGFR decline baseline-unadjusted and baseline-adjusted ("decline", n up to 343,339; declineadj, n up to 320,737).a Two variants were identified with genome-wide significance for eGFR decline baseline-unadjusted (UMOD-PDILT; Pdecline < 5 × 10–8) and 2 further variants in a candidate search of the 263 variants known for cross-sectional eGFR15Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (362) Google Scholar outside UMOD-PDILT, judged at Bonferroni-corrected significance (Pdecline < 0.05/263 = 1.90 × 10–4; PRKAG2 and SPATA7).b Five variants were identified with genome-wide significance for eGFR decline baseline-adjusted AND Bonferroni-corrected significant baseline-unadjusted (Pdecline-adj-BL < 5 × 10–8, and Pdecline < 0.05/12 = 4.17 × 10–3).c Three variants were identified with genome-wide significance for eGFR decline baseline-adjusted but not significantly associated baseline-unadjusted (Pdecline-adj-BL < 5 × 10–8, and Pdecline ≥ 4.17 × 10–3). For each identified variant, we show results for decline (baseline-unadjusted), for decline baseline-adjusted, and for cross-sectional eGFR.15Wuttke M. Li Y. Li M. et al.A catalog of genetic loci associated with kidney function from analyses of a million individuals.Nat Genet. 2019; 51: 957-972Crossref PubMed Scopus (362) Google Scholar The β estimates are in ml/min per 1.73 m2 per year and per faster-decline allele; significant P values are stated in bold.d In PRKAG2 and SPATA7 loci, variants with smallest Pdecline (rs73158188 and rs7160717, respectively) were highly correlated with these candidate-based variants (r2 = 1.00 and 0.93, respectively).e Because the TPPP locus lead variant had imputation quality <0.6 in 45% of the studies (median, 0.64), we analyzed this locus omitting the imputation quality filter (with filter: declineadj β = 0.033, P = 1.00 × 10–8; decline β = 0.015, P = 0.039; median imputation quality = 0.74).f In the C15ORF54 locus, the identified lead variant for decline was highly correlated with a second signal lead variant for cross-sectional eGFR (rs28833881; r2 = 0.90), but not with the first signal lead variant (rs12913015; r2 = 0.04). 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